Using Remote Sensing and GIS for Assessing and Mapping Land Use and Land Qualities in the Hawkesbury‐Nepean River Catchment, Australia

Abstract

A methodological framework of land resource assessment, based on integrated biophysical information of the land in GIS and remote sensing is presented. The model developed uses two primary sub-models (modules): remote sensing based land use classification, and land suitability assessment. The first module uses Landsat 7 ETM+ images to produce a land use/land cover map of the study area. A site-specific framework was developed for use in thematic information extraction involving the use of visual analysis, spectral-based (automated) procedures, and ancillary information. The second sub-model implements a fuzzy set methodology, and uses soil landscape data sets and a Digital Elevation Model as evaluation criteria. The databases used were all georeferenced and standardised in a GIS format, enabling spatial analyses for possible pairings of data sets to be performed in order to derive new information sub-sets. A test case is presented to map the spatial distribution of runoff Curve Number (CN) using soil landscape data, which were also used for land suitability evaluation, along with existing land use/land cover information. The strategies and analytical procedures developed in this research may be useful in other areas, particularly in projects related to mapping biophysical resources for sustainable land use planning and management.

Spatial-based assessment of land use, soil erosion, and water protection in the Jeneberang valley, Indonesia

“…Land use/land cover classification A methodological framework of thematic information extraction (developed by Baja et al 2002) using Landsat ETM+ images was used to produce a land use/land cover map. In this study, three bands of Landsat ETM+ images (bands 1, 2, and 3) were employed.…”

Spatial based compromise programming for multiple criteria decision making in land use planning

“…Hence, only three estimated CN figures were presented for this application: 72, 81, and 88, for hydrologic soil groups A, B, and C, respectively [6]. These values are within the range of suggested CN index for cultivated land [20].…”

“…It thus enables a continuous form of representation of land suitability for agricultural development in the area of interest. Use of fuzzy set functions for land attribute ratings can be seen in detail in our previous work [5][6][7] and also in [8,9,11]. …”

“…Then, using the guidelines provided by the USDA-SCS [32], possible pairings between soil groups and land use types, plus CN, values were derived (table 2) [6]. In the present study, CN values were determined based on the antecedent moisture condition II (see [10]).…”

“…On the basis of the goal specified above, five sets of decision criteria were selected and employed in this analysis: LSI for cropping as described in [7], erosion tolerance index (ETI) [4], runoff curve number (CN) [6], road proximity, and water proximity.…”